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워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
9,058개의 평가
2,163개의 리뷰

강좌 소개

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

최상위 리뷰

BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

SZ

Dec 20, 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,081개 리뷰 중 2051~2075

교육 기관: Robert P M

Oct 27, 2015

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.

교육 기관: Simiao L

Jan 03, 2016

2 stars because the theoretical part is ok but programming assignments are waste of time. I'm not here (and paid) to be trained to use something the instructor is trying to SELL, nor will I ever recommend this product for commercial use. I will switch to other "not recommended" packages in the later parts of this specialization.

They should put the disclaimer for Graphlab Create in the specialization page so people can be aware of this.

Besides, the sound of that Giraffe toy is really, really annoying.

교육 기관: Morten H

Feb 08, 2016

Poorly executed. Constant differences in data. tiresome to watch two supposedly very intelligent instructors amuse themselves by saying Bro and Dude. The use og graphlab is unnecessary and adds a layer of complication which adds no future value to your toolkit. Probably a lot of better executed Machine Learning courses out there

교육 기관: Daniel J

Jan 07, 2017

excessive use of GraphLab create which is not an industry standard.

교육 기관: Patryk H

Oct 14, 2015

Due to many technical issues with GraphLab lib I have to reduce acitivity in this curse for only video viewing :(.

교육 기관: Zuozhi W

Feb 08, 2017

TBH this class's experience is not good. The lecturers seem unprepared and they talk very repetitively.

교육 기관: Bruno C S d A

Jul 15, 2016

I have no doubt teachers are excelent professionals in the area, as well as great machine learning enthusiasts. However, I did not like the fact that you get limited to learn how to use a paid and (very!) expensive platform, mostly because there are many other free packages available for machine learning. Ok, the platform offered makes things easier, but if you really want to learn machine learning, you can not be limited to a platform, acting as a robot just using pre-written functions in a black box.

교육 기관: Jean T

Apr 17, 2017

Con:

(1) I feel I spent most of the time learning graphlab. Suggest replace it with standard Python as the standard tool for this class. Provide any needed additional code in standard Python.

(2) Course is better in the front end than in the back end.

(3) Week #6 is significantly more involved than previous weeks. Suggest divide Week 6 into two sessions: Neural Network and Nearest Neighbor applying neural network results (ImageNet 2012 was mentioned and not explained. Therefore the Nearest Neighbor homework assignment from the student's perspective does not have much to do with neural network other than using the results from ImageNet 2012, which was not explained in any detail anyway). This will allow more time to delve into the forward and backward propagation which should have been explained in more details.

(4) Home assignments are not best worded, especially homework assignment for Week 6. Suggest reword in shorter statements that are more to the point.

(5) Programming presentation and assignments can seem like exercise in graphlab and SFrame functions rather than machine learning.

PRO:

(1) Class presentation by Professor Fox on recommender system is detailed and clear.

(2) Classifier block diagram shown by Professor Guestrin is good, clearly distinguishing training the classifier and the subsequent use of the classification (prediction).

(3) Neural network quiz in Week 6 is excellent. It drills down on the multi-dimensional space that neural network is particularly good for.

교육 기관: Peter G

Mar 22, 2016

The teachers are easy to like, but the course content is very lightweight and will mostly teach you terminology with no real understanding.

The worst part was the assignments, which could all be solved by a little copy/paste: I didn't learn anything useful by doing them. All the actual algorithms were supplied in a separate module. More than that, many of the suggested solutions were bad coding (like collapsing 50% of the data before training, or writing sixteen special cases rather than a general function) or pointless (like training a linear classifier on pixel data).

There are better courses out there.

교육 기관: Satyam N

Mar 26, 2018

This course doesn't give any insight about the algorithms.

교육 기관: Sunaad R

Jul 30, 2018

Too much dependency on Graphlab package is bad. If we are learning the concept, we should reduce the size of the sample data. We should be using generic open packages, so that our learning can be easily demonstrated anywhere (especially interviews), and not dependent on graphlab.

교육 기관: Joseph C

Jul 29, 2018

Overly relies on a paid software (free for the course) called GraphLab. The course can be completed without GraphLab, but expect little / no responses to questions.

교육 기관: Annemarie S

May 24, 2019

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

교육 기관: Ashley

Jun 24, 2019

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

교육 기관: Matthew F

Jul 22, 2019

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

교육 기관: Keith P D C

Oct 28, 2019

Two stars because of GraphLab! Otherwise great concepts!

교육 기관: Krupesh A

Feb 15, 2019

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

교육 기관: Chandrakant M

Sep 06, 2016

I felt that I paid for demo of the Dato/Turi.

교육 기관: Kaushik M

May 01, 2016

Too many videos and not cluttered assignment codes

교육 기관: Eduardo R R

Sep 23, 2015

This course rely on commercial library. I am sorry, I don't believe the convenience of a commercial library is good for your learning. You may end up locked in.

교육 기관: Phillip B

Sep 25, 2015

Would have greatly preferred if open source tools were used.

교육 기관: Jitendra S

Apr 29, 2016

Dato tool does not even install properly.. so n´makes no sense to continue with the course. The support team fail to help in installing ... :-(

교육 기관: Darren R

Oct 13, 2015

Thoroughly disappointed to see this course based on

교육 기관: Pablo S

Jul 22, 2019

I should have read the negative reviews before wasting two full days trying, and failing, to install the required software. I urge anyone reading this to avoid this course and look for alternatives.

교육 기관: Natalia Q C

Jul 25, 2019

The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that! I want MY MONEY BACK!!!